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Primal–Dual Algorithms for Order Fulfillment at Urban Outfitters, Inc

Author

Listed:
  • John M. Andrews

    (Celect, Inc., Boston, Massachusetts 02110;)

  • Vivek F. Farias

    (Celect, Inc., Boston, Massachusetts 02110; MIT, Cambridge, Massachusetts 02139;)

  • Aryan I. Khojandi

    (Celect, Inc., Boston, Massachusetts 02110;)

  • Chad M. Yan

    (Celect, Inc., Boston, Massachusetts 02110)

Abstract

We formulate the omni-channel fulfillment problem as an online optimization problem. We propose a novel algorithm for this problem based on the primal–dual schema. Our algorithm is robust: It does not require explicit demand forecasts. This is an important practical advantage in the apparel-retail setting, where demand is volatile and unpredictable. We provide a performance analysis establishing that our algorithm admits optimal performance guarantees in the face of adversarial demand. We describe a large-scale implementation of our algorithm at Urban Outfitters, Inc. This implementation processes on average 18,000 customer orders a day and as many as 100,000 orders on peak demand days. The system has resulted in substantial savings relative to an incumbent industry-standard fulfillment optimization implementation through optimal order-fulfillment decisions that simultaneously increase turn and lower shipping costs.

Suggested Citation

  • John M. Andrews & Vivek F. Farias & Aryan I. Khojandi & Chad M. Yan, 2019. "Primal–Dual Algorithms for Order Fulfillment at Urban Outfitters, Inc," Interfaces, INFORMS, vol. 49(5), pages 355-370, September.
    • Handle: RePEc:inm:orinte:v:49:y:2019:i:5:p:355-370
    DOI: 10.1287/inte.2019.1013
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    References listed on IDEAS

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    Cited by:

    1. Hübner, Alexander & Hense, Jonas & Dethlefs, Christian, 2022. "The revival of retail stores via omnichannel operations: A literature review and research framework," European Journal of Operational Research, Elsevier, vol. 302(3), pages 799-818.
    2. Jia Guo & Burcu B. Keskin, 2023. "Designing a centralized distribution system for omni‐channel retailing," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1724-1742, June.
    3. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    4. Meng Qi & Ho‐Yin Mak & Zuo‐Jun Max Shen, 2020. "Data‐driven research in retail operations—A review," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 595-616, December.

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